I study how and why the two major types of business investment, equipment investment and structures investment, are differently linked to stock returns. I empirically show that equipment investment has a significantly stronger predictive power for stock market returns than structures investment,...

An increase in the number of asset pricing models intensifies model uncertainties in assetpricing. While a pure "model selection" (singling out a best model) can result in a loss of usefulinformation, a full “model pooling” may increase the risk of including noisy information.We make a...

Using Internet search volume of dividend-related keywords to measure investor preference for dividends that varies over time and across states, we show that dividend sentiment affects corporate policies and asset prices. Investors search more for dividends when economic conditions are poor, with...

Non-fundamental demand shocks have significant effects on asset prices, but observing these shocks is challenging. We use the exchange traded fund (ETF) primary market to study non-fundamental demand. Unique to the ETF market, specialized arbitrageurs called authorized participants correct...

We quantify disagreement about the economy with ex-ante measures of divergence of opinion among economic forecasters and investigate if economic disagreement has a significant impact on the cross-sectional pricing of individual stocks. We find a significant disagreement premium of 7.2% per...

We propose that innovative originality is a valuable organizational resource, and that owing to limited investor attention and skepticism of complexity, greater innovative originality may be undervalued. We find that firms' innovative originality strongly predicts higher, more persistent, and...

Recent studies show that loss probability (LP) is a decisive factor when people evaluate risk of assets in laboratory experiments, suggesting a positive relationship between LP and expected stock returns. This corresponds to the classical "Safety-First" principle. We find empirical support for...

In this paper, we provide insights on the prediction of asset returns via novel machine learning methodologies. Machine learning clustering-enhanced classification and regression techniques to predict future asset return movements are proposed and compared. Numerical experiments show good...

Inspired by the recent literature on rare events and their impact on asset prices, we investigate the return predictability properties of a set of variables related to the risk of tail events extracted from equity market information and measures based on credit spreads. Our variables outperform...

This paper examines the effect of structural break in long-run mean of dividend-price ratio and its impact on long-horizon returns predictive regression in the Stock Exchange of Thailand from April 1975 to December 2010. The empirical results show that there exists a structural break in...